Add application file

app.py

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+import gradio as gr
+import torch
+import os
+import requests
+import json
+import cv2
+from PIL import Image
+from timeit import default_timer as timer
+import pathlib
+import platform
+import numpy as np
+model = torch.hub.load('ultralytics/yolov5','yolov5s', pretrained=True)
+cnt = 0
+
+def LCR(bbox,x_img, y_img):
+    x1 = bbox[0]/x_img
+    x2 = bbox[2]/x_img
+    
+    
+    if x1 < 0.2 and x2 < 0.2 :
+        location = "Left"
+    elif x1 > 0.8 and x2 > 0.8:
+        location = "Right"
+    elif x1 < 0.2 and (x2 <= 0.8 and x2 >= 0.2):
+        if (x1 + x2) < 0.4:
+            location = "Left"
+        else:
+            location = "Center" 
+    elif x2 > 0.8 and (x1 <= 0.8 and x1 >= 0.2):
+        if (x1 + x2) > 1.6:
+            location = "Right"
+        else:
+            location = "Center" 
+    else:
+        location = "Center"
+    print(f"x1 {x1} x2 {x2} bbox0 {bbox[0]} bbox2 {bbox[2]} x_img {x_img} LocationLCR {location}")
+    return location
+
+def ACB(bbox, x_img, y_img, location):
+    y1 = bbox[1]/y_img
+    y2 = bbox[3]/y_img
+    if location == "Center":
+        if y1 < 0.33333 and y2 < 0.33333 :
+            location = "Above"
+        elif y1 > 0.66667 and y2 > 0.66667:
+            location = "Below"
+        elif y1 < 0.33333 and (y2 <= 0.66667 and y2 >= 0.33333):
+            if (y1 + y2) < 0.66667:
+                location = "Above"
+            else:
+                location = "Center" 
+        elif y2 > 0.66667 and (y1 <= 0.66667 and y1 >= 0.33333):
+            if (y1 + y2) > 1.33333:
+                location = "Below"
+            else:
+                location = "Center" 
+        else:
+            location = "Center"
+    else:
+        pass
+    print(f"y1 {y1} y2 {y2} bbox1 {bbox[1]} bbox3 {bbox[3]} y_img {y_img} Location{location}")
+    
+    return location
+    #print(bbox[0])
+    
+def turn_img_into_fileJSON(frame):
+    start_time = timer()
+    x_img, y_img = frame.size
+    print(x_img,y_img)
+    global cnt
+    objects = []
+
+    prediction = model(frame)
+    for det in prediction.xyxy[0]:
+        class_id = int(det[5])
+        class_name = model.names[class_id]
+        confidence = float(det[4])
+        bbox = det[:4].tolist()
+        if(confidence >= 0.5):
+            location = LCR(bbox, x_img, y_img)
+            location = ACB(bbox, x_img, y_img, location)
+            # Save the results to the list
+            objects.append({
+                'Class': class_name,
+                #'BoundingBox': bbox,
+                'Location': location,
+                'Confidence': confidence
+            })
+        with open('{:05d}.json'.format(cnt)  , 'w') as f:
+            json.dump(objects, f)
+    cnt += 1
+    pred_time = round(timer() - start_time, 5)
+    json_str = json.dumps(objects)
+    return json_str, pred_time
+#path = [["D:/cuoc_thi/object-detection/download.jpg"],["C:/Users/ACER/Pictures/mydestiny/273536337_788402492117531_8798195010554693138_n.jpg"]]
+title = "Object-detection"
+description = "An EfficientNetB2 feature extractor computer vision model to classify images of object."
+article = "Created by Ryan"
+
+# json_str1, pred_time1 = turn_img_into_fileJSON("C:/Users/ACER/Pictures/mydestiny/273536337_788402492117531_8798195010554693138_n.jpg")
+# print(json_str1, pred_time1)
+
+# json_str, pred_time = turn_img_into_fileJSON("D:/cuoc_thi/object-detection/download.jpg")
+# print(json_str, pred_time)
+
+
+# Create the Gradio demo
+demo = gr.Interface(fn=turn_img_into_fileJSON, # mapping function from input to output
+                    inputs="pil", # what are the inputs?
+                    outputs=[gr.JSON(label="JSON Output"),
+                             #gr.Label(num_top_classes=80, label="Predictions"),
+                             gr.Number(label="Prediction time (s)")],
+                             #gr.outputs.Label(num_top_classes= 80),
+                    #examples=path, 
+                    title=title,
+                    description=description,
+                    article=article,
+                    live = True)
+demo.launch()
+#demo.launch(share=True)

yolov5s.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8b3b748c1e592ddd8868022e8732fde20025197328490623cc16c6f24d0782ee
+size 14808437